Overview

Dataset statistics

Number of variables9
Number of observations17898
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory72.0 B

Variable types

Numeric8
Categorical1

Alerts

Mean of the integrated profile is highly overall correlated with Excess kurtosis of the integrated profile and 2 other fieldsHigh correlation
Standard deviation of the integrated profile is highly overall correlated with Skewness of the integrated profile and 1 other fieldsHigh correlation
Excess kurtosis of the integrated profile is highly overall correlated with Mean of the integrated profile and 2 other fieldsHigh correlation
Skewness of the integrated profile is highly overall correlated with Mean of the integrated profile and 3 other fieldsHigh correlation
Mean of the DM-SNR curve is highly overall correlated with Standard deviation of the DM-SNR curve and 3 other fieldsHigh correlation
Standard deviation of the DM-SNR curve is highly overall correlated with Mean of the DM-SNR curve and 3 other fieldsHigh correlation
Excess kurtosis of the DM-SNR curve is highly overall correlated with Mean of the DM-SNR curve and 3 other fieldsHigh correlation
Skewness of the DM-SNR curve is highly overall correlated with Mean of the DM-SNR curve and 2 other fieldsHigh correlation
target_class is highly overall correlated with Mean of the integrated profile and 6 other fieldsHigh correlation
target_class is highly imbalanced (55.8%)Imbalance
Skewness of the integrated profile has unique valuesUnique

Reproduction

Analysis started2023-05-16 07:10:59.405968
Analysis finished2023-05-16 07:11:10.229768
Duration10.82 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Mean of the integrated profile
Real number (ℝ)

Distinct8626
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.07997
Minimum5.8125
Maximum192.61719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-05-16T16:11:10.534561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.8125
5-th percentile57.491797
Q1100.92969
median115.07812
Q3127.08594
95-th percentile143.07266
Maximum192.61719
Range186.80469
Interquartile range (IQR)26.15625

Descriptive statistics

Standard deviation25.652935
Coefficient of variation (CV)0.23094115
Kurtosis2.9723738
Mean111.07997
Median Absolute Deviation (MAD)12.921875
Skewness-1.3751876
Sum1988109.3
Variance658.07309
MonotonicityNot monotonic
2023-05-16T16:11:10.693964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.7109375 12
 
0.1%
112.9140625 9
 
0.1%
106.6484375 9
 
0.1%
124.546875 9
 
0.1%
115.234375 8
 
< 0.1%
118.65625 8
 
< 0.1%
123.03125 8
 
< 0.1%
134.59375 8
 
< 0.1%
105.2421875 8
 
< 0.1%
124.4296875 8
 
< 0.1%
Other values (8616) 17811
99.5%
ValueCountFrequency (%)
5.8125 1
< 0.1%
6.1796875 1
< 0.1%
6.1875 2
< 0.1%
6.265625 1
< 0.1%
6.4140625 1
< 0.1%
6.5 1
< 0.1%
6.9375 1
< 0.1%
6.984375 1
< 0.1%
7.0390625 1
< 0.1%
7.0625 1
< 0.1%
ValueCountFrequency (%)
192.6171875 1
< 0.1%
190.421875 1
< 0.1%
189.734375 1
< 0.1%
186.0234375 1
< 0.1%
185.2578125 1
< 0.1%
184.828125 1
< 0.1%
184.4609375 1
< 0.1%
184.296875 1
< 0.1%
183.453125 1
< 0.1%
183.4140625 1
< 0.1%

Standard deviation of the integrated profile
Real number (ℝ)

Distinct17862
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549532
Minimum24.772042
Maximum98.778911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-05-16T16:11:10.866129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum24.772042
5-th percentile34.714316
Q142.376018
median46.947479
Q351.023202
95-th percentile56.473756
Maximum98.778911
Range74.006869
Interquartile range (IQR)8.6471844

Descriptive statistics

Standard deviation6.8431894
Coefficient of variation (CV)0.14700877
Kurtosis1.6895714
Mean46.549532
Median Absolute Deviation (MAD)4.289352
Skewness0.12664108
Sum833143.52
Variance46.829241
MonotonicityNot monotonic
2023-05-16T16:11:11.019127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.84382297 2
 
< 0.1%
43.40470885 2
 
< 0.1%
46.52196222 2
 
< 0.1%
51.04274918 2
 
< 0.1%
40.51518812 2
 
< 0.1%
43.81001394 2
 
< 0.1%
44.44872562 2
 
< 0.1%
44.39730953 2
 
< 0.1%
48.62217818 2
 
< 0.1%
45.62602471 2
 
< 0.1%
Other values (17852) 17878
99.9%
ValueCountFrequency (%)
24.77204176 1
< 0.1%
24.79161196 1
< 0.1%
24.89821075 1
< 0.1%
25.22005568 1
< 0.1%
25.69524955 1
< 0.1%
25.77171107 1
< 0.1%
26.12268115 1
< 0.1%
26.17979708 1
< 0.1%
26.33786912 1
< 0.1%
26.42932493 1
< 0.1%
ValueCountFrequency (%)
98.77891067 1
< 0.1%
91.8086279 1
< 0.1%
91.20647473 1
< 0.1%
90.25055726 1
< 0.1%
90.15744556 1
< 0.1%
86.95139648 1
< 0.1%
86.23983041 1
< 0.1%
85.97090118 1
< 0.1%
85.79734025 1
< 0.1%
85.32084974 1
< 0.1%

Excess kurtosis of the integrated profile
Real number (ℝ)

Distinct17897
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47785726
Minimum-1.8760112
Maximum8.069522
Zeros0
Zeros (%)0.0%
Negative3898
Negative (%)21.8%
Memory size140.0 KiB
2023-05-16T16:11:11.174981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.8760112
5-th percentile-0.23167082
Q10.02709812
median0.22324002
Q30.47332518
95-th percentile2.7065609
Maximum8.069522
Range9.9455332
Interquartile range (IQR)0.44622706

Descriptive statistics

Standard deviation1.0640397
Coefficient of variation (CV)2.2266895
Kurtosis14.639742
Mean0.47785726
Median Absolute Deviation (MAD)0.21669795
Skewness3.6384097
Sum8552.6892
Variance1.1321805
MonotonicityNot monotonic
2023-05-16T16:11:11.336824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001934282 2
 
< 0.1%
-0.234571412 1
 
< 0.1%
0.369225064 1
 
< 0.1%
0.138199825 1
 
< 0.1%
0.096401948 1
 
< 0.1%
0.274500261 1
 
< 0.1%
-0.054115317 1
 
< 0.1%
0.89233225 1
 
< 0.1%
0.576817027 1
 
< 0.1%
-0.081760445 1
 
< 0.1%
Other values (17887) 17887
99.9%
ValueCountFrequency (%)
-1.876011181 1
< 0.1%
-1.738020762 1
< 0.1%
-1.730781724 1
< 0.1%
-1.707789078 1
< 0.1%
-1.679039339 1
< 0.1%
-1.669032278 1
< 0.1%
-1.64151544 1
< 0.1%
-1.633922495 1
< 0.1%
-1.624269471 1
< 0.1%
-1.604829088 1
< 0.1%
ValueCountFrequency (%)
8.069522046 1
< 0.1%
7.879627678 1
< 0.1%
7.875742091 1
< 0.1%
7.856370386 1
< 0.1%
7.627580248 1
< 0.1%
7.60836954 1
< 0.1%
7.595096784 1
< 0.1%
7.572576517 1
< 0.1%
7.550921894 1
< 0.1%
7.525027544 1
< 0.1%

Skewness of the integrated profile
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct17898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.770279
Minimum-1.791886
Maximum68.101622
Zeros0
Zeros (%)0.0%
Negative6850
Negative (%)38.3%
Memory size140.0 KiB
2023-05-16T16:11:11.489821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.791886
5-th percentile-0.55637131
Q1-0.18857165
median0.1987104
Q30.92778309
95-th percentile10.138507
Maximum68.101622
Range69.893508
Interquartile range (IQR)1.1163547

Descriptive statistics

Standard deviation6.1679132
Coefficient of variation (CV)3.4841476
Kurtosis30.166479
Mean1.770279
Median Absolute Deviation (MAD)0.47309534
Skewness5.1812934
Sum31684.454
Variance38.043154
MonotonicityNot monotonic
2023-05-16T16:11:11.634088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.699648398 1
 
< 0.1%
0.377322825 1
 
< 0.1%
-0.681334922 1
 
< 0.1%
0.224088239 1
 
< 0.1%
0.853357488 1
 
< 0.1%
-0.097100568 1
 
< 0.1%
1.519544617 1
 
< 0.1%
0.098896545 1
 
< 0.1%
0.876420451 1
 
< 0.1%
-0.259967627 1
 
< 0.1%
Other values (17888) 17888
99.9%
ValueCountFrequency (%)
-1.791885981 1
< 0.1%
-1.781888301 1
< 0.1%
-1.764717446 1
< 0.1%
-1.755331667 1
< 0.1%
-1.676724149 1
< 0.1%
-1.668540363 1
< 0.1%
-1.660049111 1
< 0.1%
-1.60297669 1
< 0.1%
-1.598144586 1
< 0.1%
-1.593648457 1
< 0.1%
ValueCountFrequency (%)
68.10162173 1
< 0.1%
65.38597385 1
< 0.1%
63.46638835 1
< 0.1%
63.14953741 1
< 0.1%
62.86853087 1
< 0.1%
58.2945011 1
< 0.1%
57.50455774 1
< 0.1%
57.17523165 1
< 0.1%
57.07049316 1
< 0.1%
56.90085222 1
< 0.1%

Mean of the DM-SNR curve
Real number (ℝ)

Distinct9000
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.6144
Minimum0.2132107
Maximum223.39214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-05-16T16:11:11.791017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2132107
5-th percentile1.0058528
Q11.9230769
median2.8018395
Q35.4642559
95-th percentile82.966973
Maximum223.39214
Range223.17893
Interquartile range (IQR)3.5411789

Descriptive statistics

Standard deviation29.472897
Coefficient of variation (CV)2.3364487
Kurtosis14.064721
Mean12.6144
Median Absolute Deviation (MAD)1.1780936
Skewness3.6833021
Sum225772.53
Variance868.65167
MonotonicityNot monotonic
2023-05-16T16:11:11.935460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.294314381 12
 
0.1%
1.423913043 12
 
0.1%
2.050167224 12
 
0.1%
2.33277592 12
 
0.1%
2.193979933 11
 
0.1%
2.418896321 11
 
0.1%
2.060200669 10
 
0.1%
2.211538462 10
 
0.1%
2.0409699 10
 
0.1%
1.777591973 10
 
0.1%
Other values (8990) 17788
99.4%
ValueCountFrequency (%)
0.213210702 4
< 0.1%
0.24916388 1
 
< 0.1%
0.273411371 1
 
< 0.1%
0.282608696 1
 
< 0.1%
0.289297659 1
 
< 0.1%
0.2909699 1
 
< 0.1%
0.300167224 2
< 0.1%
0.31270903 1
 
< 0.1%
0.316053512 1
 
< 0.1%
0.317725753 2
< 0.1%
ValueCountFrequency (%)
223.3921405 1
< 0.1%
222.4214047 1
< 0.1%
217.3712375 1
< 0.1%
211.9489967 1
< 0.1%
209.3001672 1
< 0.1%
208.6295987 1
< 0.1%
207.3026756 1
< 0.1%
206.5292642 1
< 0.1%
203.8177258 1
< 0.1%
202.3319398 1
< 0.1%

Standard deviation of the DM-SNR curve
Real number (ℝ)

Distinct17894
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.326515
Minimum7.3704322
Maximum110.64221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-05-16T16:11:12.079822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7.3704322
5-th percentile11.082345
Q114.437332
median18.461316
Q328.428104
95-th percentile74.621642
Maximum110.64221
Range103.27178
Interquartile range (IQR)13.990773

Descriptive statistics

Standard deviation19.470572
Coefficient of variation (CV)0.73958033
Kurtosis2.8259975
Mean26.326515
Median Absolute Deviation (MAD)5.1622179
Skewness1.8942541
Sum471191.96
Variance379.10319
MonotonicityNot monotonic
2023-05-16T16:11:12.222622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.370432165 4
 
< 0.1%
11.57512195 2
 
< 0.1%
19.11042633 1
 
< 0.1%
11.3198745 1
 
< 0.1%
12.52424688 1
 
< 0.1%
17.71842096 1
 
< 0.1%
70.2714504 1
 
< 0.1%
75.29602001 1
 
< 0.1%
11.42201394 1
 
< 0.1%
14.42902093 1
 
< 0.1%
Other values (17884) 17884
99.9%
ValueCountFrequency (%)
7.370432165 4
< 0.1%
7.4488166 1
 
< 0.1%
7.473461921 1
 
< 0.1%
7.564949538 1
 
< 0.1%
7.565661683 1
 
< 0.1%
7.565681088 1
 
< 0.1%
7.656919973 1
 
< 0.1%
7.658622807 1
 
< 0.1%
7.663910248 1
 
< 0.1%
7.664622639 1
 
< 0.1%
ValueCountFrequency (%)
110.6422106 1
< 0.1%
109.7126491 1
< 0.1%
109.6553451 1
< 0.1%
108.9314268 1
< 0.1%
108.7108265 1
< 0.1%
108.0780191 1
< 0.1%
107.9474895 1
< 0.1%
107.4520459 1
< 0.1%
107.4318232 1
< 0.1%
106.7991743 1
< 0.1%

Excess kurtosis of the DM-SNR curve
Real number (ℝ)

Distinct17895
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3035561
Minimum-3.1392696
Maximum34.539844
Zeros0
Zeros (%)0.0%
Negative574
Negative (%)3.2%
Memory size140.0 KiB
2023-05-16T16:11:12.366622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3.1392696
5-th percentile0.53090761
Q15.7815057
median8.4335147
Q310.702959
95-th percentile15.77068
Maximum34.539844
Range37.679114
Interquartile range (IQR)4.9214535

Descriptive statistics

Standard deviation4.5060919
Coefficient of variation (CV)0.54267013
Kurtosis1.5262094
Mean8.3035561
Median Absolute Deviation (MAD)2.4240376
Skewness0.44150087
Sum148617.05
Variance20.304864
MonotonicityNot monotonic
2023-05-16T16:11:12.516622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.53984419 4
 
< 0.1%
7.975531794 1
 
< 0.1%
12.15196373 1
 
< 0.1%
14.42262858 1
 
< 0.1%
11.60365846 1
 
< 0.1%
8.303748788 1
 
< 0.1%
-0.639879445 1
 
< 0.1%
-1.169558054 1
 
< 0.1%
14.86292364 1
 
< 0.1%
10.02978652 1
 
< 0.1%
Other values (17885) 17885
99.9%
ValueCountFrequency (%)
-3.139269611 1
< 0.1%
-2.812353306 1
< 0.1%
-2.721857186 1
< 0.1%
-2.636857381 1
< 0.1%
-2.597871861 1
< 0.1%
-2.556795187 1
< 0.1%
-2.545733541 1
< 0.1%
-2.542025366 1
< 0.1%
-2.526429634 1
< 0.1%
-2.449008501 1
< 0.1%
ValueCountFrequency (%)
34.53984419 4
< 0.1%
33.4897547 1
 
< 0.1%
33.27341088 1
 
< 0.1%
32.19858411 1
 
< 0.1%
32.17418904 1
 
< 0.1%
32.11141593 1
 
< 0.1%
31.47155929 1
 
< 0.1%
31.31226734 1
 
< 0.1%
30.99291931 1
 
< 0.1%
30.88388219 1
 
< 0.1%

Skewness of the DM-SNR curve
Real number (ℝ)

Distinct17895
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.85771
Minimum-1.9769756
Maximum1191.0008
Zeros0
Zeros (%)0.0%
Negative1139
Negative (%)6.4%
Memory size140.0 KiB
2023-05-16T16:11:12.674821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1.9769756
5-th percentile-0.49972026
Q134.960504
median83.064556
Q3139.30933
95-th percentile296.37905
Maximum1191.0008
Range1192.9778
Interquartile range (IQR)104.34883

Descriptive statistics

Standard deviation106.51454
Coefficient of variation (CV)1.0158008
Kurtosis13.494113
Mean104.85771
Median Absolute Deviation (MAD)51.515107
Skewness2.7345136
Sum1876743.3
Variance11345.347
MonotonicityNot monotonic
2023-05-16T16:11:12.834822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1191.000837 4
 
< 0.1%
74.24222492 1
 
< 0.1%
192.4558237 1
 
< 0.1%
260.0813722 1
 
< 0.1%
176.6924748 1
 
< 0.1%
81.02410076 1
 
< 0.1%
-0.977560471 1
 
< 0.1%
-0.130999459 1
 
< 0.1%
262.0944803 1
 
< 0.1%
124.9713288 1
 
< 0.1%
Other values (17885) 17885
99.9%
ValueCountFrequency (%)
-1.976975603 1
< 0.1%
-1.964997899 1
< 0.1%
-1.949108868 1
< 0.1%
-1.948954964 1
< 0.1%
-1.946039119 1
< 0.1%
-1.944969025 1
< 0.1%
-1.939238369 1
< 0.1%
-1.938422805 1
< 0.1%
-1.938052411 1
< 0.1%
-1.937552714 1
< 0.1%
ValueCountFrequency (%)
1191.000837 4
< 0.1%
1140.353233 1
 
< 0.1%
1126.765431 1
 
< 0.1%
1072.957979 1
 
< 0.1%
1072.793069 1
 
< 0.1%
1071.604226 1
 
< 0.1%
1027.555166 1
 
< 0.1%
1022.201175 1
 
< 0.1%
1017.403028 1
 
< 0.1%
1017.38318 1
 
< 0.1%

target_class
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
0
16259 
1
1639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17898
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Length

2023-05-16T16:11:12.975822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-16T16:11:13.115008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17898
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16259
90.8%
1 1639
 
9.2%

Interactions

2023-05-16T16:11:08.717282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:00.533364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.700790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.891537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.156290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.365271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.424694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.511737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:08.861282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:00.717089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.851805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.034533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.307287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.526457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.565596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.657733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.020885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:00.882405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.023646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.174533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.485146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.672677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.709597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.798732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.144437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.017402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.176647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.484810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.626144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.800914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.852884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.933732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.279437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.157032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.323647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.628810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.773144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.939915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.995897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:08.077891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.426437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.285639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.462566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.752810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.909144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.052692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.117900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:08.238893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.548586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.416792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.593565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:03.880506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.054270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.168692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.234899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:08.402894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:09.701588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:01.562790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:02.746566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:04.024287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:05.207270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:06.299692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:07.375899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-16T16:11:08.571286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-16T16:11:13.210007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Mean of the integrated profileStandard deviation of the integrated profileExcess kurtosis of the integrated profileSkewness of the integrated profileMean of the DM-SNR curveStandard deviation of the DM-SNR curveExcess kurtosis of the DM-SNR curveSkewness of the DM-SNR curvetarget_class
Mean of the integrated profile1.0000.499-0.880-0.635-0.077-0.0900.0810.0850.794
Standard deviation of the integrated profile0.4991.000-0.496-0.8760.007-0.0050.0030.0050.517
Excess kurtosis of the integrated profile-0.880-0.4961.0000.6570.0870.100-0.089-0.0930.866
Skewness of the integrated profile-0.635-0.8760.6571.0000.0650.078-0.072-0.0750.800
Mean of the DM-SNR curve-0.0770.0070.0870.0651.0000.950-0.991-0.9860.503
Standard deviation of the DM-SNR curve-0.090-0.0050.1000.0780.9501.000-0.946-0.9720.573
Excess kurtosis of the DM-SNR curve0.0810.003-0.089-0.072-0.991-0.9461.0000.9940.514
Skewness of the DM-SNR curve0.0850.005-0.093-0.075-0.986-0.9720.9941.0000.201
target_class0.7940.5170.8660.8000.5030.5730.5140.2011.000

Missing values

2023-05-16T16:11:09.890590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-16T16:11:10.113765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Mean of the integrated profileStandard deviation of the integrated profileExcess kurtosis of the integrated profileSkewness of the integrated profileMean of the DM-SNR curveStandard deviation of the DM-SNR curveExcess kurtosis of the DM-SNR curveSkewness of the DM-SNR curvetarget_class
0140.56250055.683782-0.234571-0.6996483.19983319.1104267.97553274.2422250
1102.50781258.8824300.465318-0.5150881.67725814.86014610.576487127.3935800
2103.01562539.3416490.3233281.0511643.12123721.7446697.73582263.1719090
3136.75000057.178449-0.068415-0.6362383.64297720.9592806.89649953.5936610
488.72656240.6722250.6008661.1234921.17893011.46872014.269573252.5673060
593.57031246.6981140.5319050.4167211.63628814.54507410.621748131.3940040
6119.48437548.7650590.031460-0.1121680.9991649.27961219.206230479.7565670
7130.38281239.844056-0.1583230.3895401.22073614.37894113.539456198.2364570
8107.25000052.6270780.4526880.1703472.33194014.4868539.001004107.9725060
9107.25781239.4964880.4658821.1628774.07943124.9804187.39708057.7847380
Mean of the integrated profileStandard deviation of the integrated profileExcess kurtosis of the integrated profileSkewness of the integrated profileMean of the DM-SNR curveStandard deviation of the DM-SNR curveExcess kurtosis of the DM-SNR curveSkewness of the DM-SNR curvetarget_class
1788898.72656250.4078230.5651240.2452310.5702349.01128522.018589561.8337870
17889126.62500055.7218260.002946-0.3032180.5342818.58888223.913761660.1970350
17890143.67187545.302647-0.0457690.3536435.17391326.4623455.70665133.8026130
17891118.48437550.608483-0.029059-0.0274940.4222418.08668427.446113830.6385500
1789296.00000044.1931130.3886740.2813441.87123715.8337469.634927104.8216230
17893136.42968859.847421-0.187846-0.7381231.29682312.16606215.450260285.9310220
17894122.55468849.4856050.1279780.32306116.40969944.6268932.9452448.2970920
17895119.33593859.9359390.159363-0.74302521.43060258.8720002.4995174.5951730
17896114.50781253.9024000.201161-0.0247891.94648813.38173110.007967134.2389100
1789757.06250085.7973401.4063910.089520188.30602064.712562-1.5975271.4294750